Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

The Black-White Achievement Gap Amongst Struggling Readers: A Case Study of Early Intervention Outcomes in Massachusetts

A number of analyses of large data sets have suggested that the reading achievement gap between African American and White U.S. is negligible or small at school entry, but widens substantially during the school years because African American students show slower rates of growth in elementary and secondary school. Identifying when and why gaps occur, therefore, is a an important research endeavor. In addition, being able to predict which African American children are most likely to fall behind can contribute to efforts to close the achievement gap. This paper analyzes first grade and third grade data on African American and White children in Massachusetts who all were identified in first grade as struggling readers and enrolled in Reading Recovery—an individualized intervention. All the children were low-income and attending urban schools. Using Observation Survey data from first grade, and MCAS Reading data from 3rd grade, we found that the African American and White students made equal average progress while in first grade, but by the end of third grade showed a large gap in MCAS proficiency rates. We discuss the results in terms of school quality, reading development, dialect issues, testing formats, and the need to provide long-term support to vulnerable learners.

Effects of Study Area Size on Geographic Characterizations of Health Events: Prostate Cancer Incidence in Southern New England, USA, 1994-1998.

ABSTRACT : BACKGROUND : We consider how representations of geographic variation in prostate cancer incidence across Southern New England, USA may be affected by selection of study area and/or properties of the statistical analysis. METHOD : A spatial scan statistic was used to monitor geographic variation among 35,167 incident prostate cancer cases diagnosed in Massachusetts, Connecticut and Rhode Island from 1994 to 1998, in relation to the 1990 populations of men 20+ years of age living in that region. Results from the combined-states analysis were compared to those from single-states. Impact of scanning procedures set to examine up to 50% or no more than10% of at-risk populations also was evaluated. RESULTS : With scanning set to 50%, 5 locations in the combined-states analysis were identified with markedly distinct incidence rates. Fewer than expected cases were estimated for nearly all Connecticut, Rhode Island and West Central Massachusetts, whereas census tracts on and around Cape Cod, and areas of Southwestern Connecticut and adjacent to greater Boston were estimated to have yielded more than expected incidence. Results of single-state analyses exhibited several discrepancies from the combined-states analysis. More conservative scanning found many more locations with varying incidence, but discrepancies between the combined- and single-state analysis were fewer. CONCLUSION : It is important to acknowledge the conditional nature of spatial analyses and carefully consider whether a true cluster of events is identified or artifact stemming from selection of study area size and/or scanning properties.